• East Asian mathematical journal
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• v.25 no.2
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• pp.147-151
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• 2009

We use an improved Newton-Kantorovich theorem introduced in [2] to analyze interior point methods. Our approach requires less number of steps than before [5] to achieve a certain error tolerance for both Newton's and Modified Newton's methods.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.369-378
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• 1999

In This study we examine the applicability of Newton's method and the modified Newton's method for a, pp.oximating a lo-cally unique solution of a nonlinear equation in a Banach space. We assume that the newton-Kantorovich hypothesis for Newton's method is violated but the corresponding condition for the modified Newton method holds. Under these conditions there is no guaran-tee that Newton's method starting from the same initial guess as the modified Newton's method converges. Hence it seems that we must always use the modified Newton method under these condi-tions. However we provide a numerical example to demonstrate that in practice this may not be a good decision.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.339-344
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• 2000

Newton-Raphson 기법은 구조물의 비선형 해석에 널리 쓰이는 반복계산기법이다. 비선형 해석을 위한 반복계산기법은 컴퓨터의 발달을 감안해도 상당한 계산시간이 소요된다. 본 논문에서는 신경회로망 예측을 사용한 Predicted Newton-Raphson 반복계산기법을 제안하였다. 통상적인 Newton-Raphson 기법은 이전스텝에서 수렴된 점으로부터 현재 스텝의 반복계산을 시작하는 반면 제시된 방법은 현재 스텝 수렴해에 대한 예측점에서 반복계산을 시작한다. 수렴해에 대한 예측은 신경회로망을 사용하여 이전 스텝 수렴해의 과거경향을 파악한 후 구한다. 반복계산 시작점이 수렴점에 보다 근접하여 위치하므로 수렴속도가 빨라지게 되고 허용되는 하중스텝의 크기가 커지게 된다. 또한 반복계산의 시작점으로부터 이루어지는 계산과정은 통상적인 Newton-Raphson 기법과 동일하므로 기존의 Newton-Raphson 기법과 정확히 일치하는 수렴해를 구할 수 있다. 구조물의 정적 비선형 거동에 대한 수치해석을 통하여 modified Newton-Raphson 기법과 제시된 Predicted Newton=Raphson 기법의 정확성과 효율성을 비교하였다. 제시된 Predicted Newton-Raphson 기법은 modified Newton-Raphson 기법과 동일한 해를 산출하면서도 계산상의 효율성이 매우 큼을 확인할 수 있었다.

• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.747-756
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• 1999

The Newtom method and the quasi-Newton method for solving equations of smooth compositions of semismooth functions are proposed. The Q-superlinear convergence of the Newton method and the Q-linear convergence of the quasi-Newton method are proved. The present methods can be more easily implemeted than previous ones for this class of nonsmooth equations.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.3 s.345
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• pp.15-20
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• 2006

A Newton-Raphson Method for table driven algorithm is presented in this paper. We concentrate the approximation of square root by using Newton-Raphson method. We confirm that this method has advantages of accurate and fast processing with optimized initial point. Hence the selection of the fitted initial points used in approximation of Newton-Raphson algorithm is important issue. This paper proposes that log scale based on geometric wean is most profitable initial point. It shows that the proposed method givemore accurate results with faster processing speed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.1
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• pp.19-30
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• 2011

In [21], we compared the Newton-Krylov method and some high-order methods to solve nonlinear systems. In this paper, we propose high-order Newton-Krylov methods combining the Newton-Krylov method with some high-order iterative methods to solve systems of nonlinear equations. We provide some numerical experiments including comparisons of CPU time and iteration numbers of the proposed high-order Newton-Krylov methods for several nonlinear systems.

• Journal for History of Mathematics
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• v.15 no.3
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• pp.25-34
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• 2002

This paper explains methods of numerical analysis which appear on Cardano's Ars Magna and Newton's Principia. Cardano's method is secant method, but its actual al]plication is severely limited by technical difficulties. Newton's method is what nowadays called Newton-Raphson's method. But mysteriously, Newton's explanation had been forgotten for two hundred years, until Adams rediscovered it. Newton had even explained finding the root using the second degree Taylor's polynomial, which shows Newton's greatness.

• East Asian mathematical journal
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• v.24 no.3
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• pp.289-293
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• 2008

We recently showed in [5] a semilocal convergence theorem that guarantees convergence of Newton's method to a locally unique solution of a nonlinear equation under hypotheses weaker than those of the Newton-Kantorovich theorem [7]. Here, we first weaken Miranda's theorem [1], [9], [10], which is a generalization of the intermediate value theorem. Then, we show that operators satisfying the weakened Newton-Kantorovich conditions satisfy those of the weakened Miranda’s theorem.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.1-17
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• 2004

The famous Newton-Kantorovich hypothesis has been used for a long time as a sufficient condition for the convergence of Newton method to a solution of an equation in connection with the Lipschitz continuity of the Frechet-derivative of the operator involved. Using Lipschitz and center-Lipschitz conditions we show that the Newton-Kantorovich hypothesis is weakened. The error bounds obtained under our semilocal convergence result are finer and the information on the location of the solution more precise than the corresponding ones given by the dominating Newton-Kantorovich theorem, and under the same hypotheses/computational cost, since the evaluation of the Lipschitz also requires the evaluation of the center-Lipschitz constant. In the case of local convergence we obtain a larger convergence radius than before. This observation is important in computational mathematics and can be used in connection to projection methods and in the construction of optimum mesh independence refinement strategies.

• The Pure and Applied Mathematics
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• v.16 no.4
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• pp.405-416
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• 2009

In this study we are concerned with the problem of approximating a locally unique solution of an equation in a Banach space setting using Newton's and modified Newton's methods. We provide weaker convergence conditions for both methods than before [5]-[7]. Then, we combine Newton's with the modified Newton's method to approximate locally unique solutions of operator equations. Finer error estimates, a larger convergence domain, and a more precise information on the location of the solution are obtained under the same or weaker hypotheses than before [5]-[7]. The results obtained here improve our earlier ones reported in [4]. Numerical examples are also provided.